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This paper presents a Distributed Ride-Sharing Framework (DRSF) designed to enhance the efficiency of driver-rider matching in large-scale road networks. Unlike traditional centralized ride-sharing systems, DRSF partitions the road network into multiple sub-networks, each managed by a separate compute node. These compute nodes process local matching in parallel using the Multi-Capacity Matching (MCM) algorithm, and then send their matching results to a central server, which merges all the results. In addition, the modular design of DRSF supports the integration of existing matching algorithms. Extensive experiments on two real-world datasets show that DRSF significantly improves computational efficiency compared to the current state-of-the-art centralized methods.

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An Effective Distributed Ride-Sharing Framework on Large-Scale Road Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Junwei Liu,
  • Detian Zhang

摘要

This paper presents a Distributed Ride-Sharing Framework (DRSF) designed to enhance the efficiency of driver-rider matching in large-scale road networks. Unlike traditional centralized ride-sharing systems, DRSF partitions the road network into multiple sub-networks, each managed by a separate compute node. These compute nodes process local matching in parallel using the Multi-Capacity Matching (MCM) algorithm, and then send their matching results to a central server, which merges all the results. In addition, the modular design of DRSF supports the integration of existing matching algorithms. Extensive experiments on two real-world datasets show that DRSF significantly improves computational efficiency compared to the current state-of-the-art centralized methods.